Implementation details.
By default, our feature sampling is implemented as RoIAlign with a feature resolution of 4. Three different feature levels are included for multi-scale feature fusion. A fixed repeat time of 2 is adopted for our label augmentation and non-maximum suppression (NMS) with a threshold of 0.7 is used for duplicate removal. All models are trained for 50 epochs and the learning rate decays after 40 epochs, unless specified.
ResNet-50 [1]} pre-trained on ImageNet-1K [2]} is used as backbone. To guarantee enough number of training iterations, all experiments on Cityscapes and down-sampled COCO 2017 datasets are trained with a batch size of 8. And the results are averaged over five repeated runs with different random seeds. Our data-efficient detection transformers only make slight modifications to existing methods. Unless specified, we follow the original implementation details of corresponding baseline methods [3]}, [4]}. Run time is evaluated on NVIDIA A100 GPU.
